Overage as an output in clinical trial supply management

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Doménica Torres
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One of the most challenging elements in clinical trial supply management is planning correctly for buffer stock or overage.

Since randomized clinical trials are inherently uncertain when it comes to patient demand, buffer stock is necessary to ensure that the right medications are on-site for patient dispensing. However, when buffers are not consumed within the expected timeframe during the trial, the result is unused drugs. Furthermore, there has been increased pressure to reduce the overage used in clinical trial supplies management to reduce drug waste, due to the rise in the costs of production of biotech drugs. Nonetheless, it is not an easy task to determine the exact amount of overage that will be needed to avoid any risk that patients do not receive their medicine on time. 

Consequently, it is imperative to have a reliable model to assess the uncertainty in a trial and to be aware of the risks associated with any given clinical trial supplies management strategy. Without such a model, guesswork would become inevitable. The approaches generally used by clinical trial forecasting solutions to assign overage to a clinical trial can lead to either inefficient planning with increased waste and cost, or potential risk to patient dispensing. 

Clinical trial optimization model

Risk-based clinical trial optimization assesses risks at all stages of the clinical trial supply chain. From the earliest stage of manufacturing to patient dispensing, risk-based optimization increases visibility on any risk, whether it is a patient missing a kit at site level or a drug shortage at a depot.

Applying risk-based optimization in clinical trial supplies management takes the guesswork out of assessing the required overage

This solution assesses accurately how much overage is needed for a specific trial by quantifying the risk in any given supply strategy for the clinical trial protocol and evaluating the likelihood that the right medication will be on-site and on time for dispensing to the patient.

“As opposed to clinical supply forecasting, risk-based optimization will not ask you to guess the overage needed to safely supply your trial. Based on your trial design, it will find the lowest possible overage required to avoid risks of drug shortage.”

Amaury Jeandrain, Head of Life Sciences Solutions Adoption at N-SIDE

A risk-based optimization solution must achieve these elements in order to address the needs of the clinical trial supplies management:

  • Capture all the details of the clinical trial protocol and the clinical assumptions to model and simulate the trial
  • Be efficient enough to run many simulations capable of capturing the different ways that the trial could unfold based on clinical assumptions and protocol design
  • Provide results that indicate how to implement the best fit clinical trial supplies management strategy
  • Easily monitor the accuracy of the clinical supply forecasts while comparing them with the study actuals. This allows managers to re-run simulations based on real-time trial data, and update supply decisions as needed.

The N-SIDE solutions, which includes the Supply App, is a platform that delivers overage as an output of optimization, thus providing a high level of confidence in managing risk and reducing waste in clinical trial supplies management. 

The N-SIDE Supply App provides the necessary level of overage as a result, as well as all of the information on any potential risk in that supply strategy. The resulting overage is part of the overall strategy of IRT settings, packaging planning, and depot resupply strategy. Using the N-SIDE Supply App has proven to reduce drug waste by up to 40% while at the same time proactively mitigating 100% of the risk of medication not being available on-time for patients.

Thanks to the risk-based optimization for clinical trial supplies management provided by the Supply App, Clinical Supply Managers are able to focus on managing their trials in a more efficient way, without spending time guessing what level of overage will be needed to cover the risk for patients.


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Doménica Torres

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